復(fù)雜網(wǎng)絡(luò)社團(tuán)探測方法及在輪機(jī)故障診斷中應(yīng)用的研究
發(fā)布時間：2018-11-11 20:17
【摘要】：復(fù)雜網(wǎng)絡(luò)作為一門結(jié)合了數(shù)學(xué)、物理學(xué)、計算機(jī)圖形學(xué)和社會學(xué)等多種知識的新興技術(shù),是21世紀(jì)各領(lǐng)域研究人員關(guān)注的一個重點。復(fù)雜網(wǎng)絡(luò)由大量的節(jié)點和邊組成,絕大部分真實網(wǎng)絡(luò)都由一些內(nèi)部連接稠密而彼此之間連接稀疏的節(jié)點群組成,即具有社團(tuán)結(jié)構(gòu)。社團(tuán)探測是復(fù)雜網(wǎng)絡(luò)理論的一個重要研究方向,幫助人們從中觀角度了解復(fù)雜系統(tǒng)及其代表的各種現(xiàn)象。Newman快速算法與標(biāo)簽傳播算法是兩種經(jīng)典的社團(tuán)探測方法,由于探測速度快且不需要預(yù)先指定社團(tuán)數(shù)目,得到了普遍的關(guān)注。社團(tuán)探測方法的應(yīng)用多在于真實網(wǎng)絡(luò)聚類,對于聚類問題的另一大分支——數(shù)據(jù)聚類則研究甚少,而數(shù)據(jù)聚類一直是解決船舶柴油機(jī)故障診斷問題的一個重要手段。船舶柴油機(jī)是船舶的心臟,利用Newman快速算法和標(biāo)簽傳播算法的優(yōu)勢解決船舶柴油機(jī)故障診斷問題對維護(hù)航行安全有著重要意義。本論文從實際應(yīng)用的需求出發(fā),研究了標(biāo)簽傳播算法的推廣與改進(jìn)策略和基于社團(tuán)探測理論的船舶柴油機(jī)故障診斷方法,主要研究工作包含以下幾方面內(nèi)容。1.利用Newman快速算法在聚類問題中自行確定類數(shù)的特點,提出基于Newman快速算法的船舶柴油機(jī)故障診斷方法。以樣本為節(jié)點、樣本間相似度為邊權(quán),構(gòu)建有權(quán)無向的復(fù)雜網(wǎng)絡(luò),并以Newman快速算法中的準(zhǔn)則函數(shù)作為自底向上的層次聚類的準(zhǔn)則函數(shù),建立聚類方法模型,對船舶柴油機(jī)故障樣本進(jìn)行數(shù)據(jù)聚類,并使用聚類結(jié)果對待識別樣本進(jìn)行故障類型識別。診斷實例和影響因素試驗結(jié)果表明,該方法對類數(shù)等初始條件要求低、運行時間短、準(zhǔn)確率高且具有一定的穩(wěn)定性,能夠識別出歷史數(shù)據(jù)中不存在的故障類型。2.為提高標(biāo)簽傳播算法的實用性,推廣了標(biāo)簽傳播算法,使其適用于有權(quán)網(wǎng)絡(luò),從而能夠用于船舶柴油機(jī)故障數(shù)據(jù)聚類。通過分析得知標(biāo)簽傳播算法的三個關(guān)鍵因素為標(biāo)簽初始分配、標(biāo)簽傳播規(guī)則和傳播終止條件,根據(jù)多重邊的原則計算兩相鄰節(jié)點同社團(tuán)的概率,加權(quán)了標(biāo)簽傳播規(guī)則和標(biāo)簽傳播的終止條件,從而將標(biāo)簽傳播算法推廣到有權(quán)情況。網(wǎng)絡(luò)社團(tuán)探測試驗結(jié)果表明,推廣后的標(biāo)簽傳播算法適用于有權(quán)網(wǎng)絡(luò)社團(tuán)探測；同時經(jīng)典測試數(shù)據(jù)集和柴油機(jī)供油系統(tǒng)故障數(shù)據(jù)集的聚類試驗結(jié)果表明,推廣后的標(biāo)簽傳播算法適用于數(shù)據(jù)聚類。3.針對標(biāo)簽傳播過程中容易出現(xiàn)平凡解的問題,提出了基于逾滲轉(zhuǎn)變預(yù)測過程的標(biāo)簽傳播算法。原標(biāo)簽傳播算法的隨機(jī)性導(dǎo)致了平凡解的出現(xiàn),影響了算法的速度和準(zhǔn)確性。通過轉(zhuǎn)化標(biāo)簽傳播過程為網(wǎng)絡(luò)構(gòu)建過程,將隨機(jī)網(wǎng)絡(luò)生成過程中的逾滲轉(zhuǎn)變現(xiàn)象與平凡解的出現(xiàn)聯(lián)系起來,從而通過在標(biāo)簽傳播過程中添加逾滲轉(zhuǎn)變的預(yù)測過程來減少平凡解的出現(xiàn)。推廣鄰居純度的概念到有權(quán)網(wǎng)絡(luò),并給出考慮被更新標(biāo)簽的節(jié)點度的不完全更新條件來節(jié)省計算時間。網(wǎng)絡(luò)試驗結(jié)果表明改進(jìn)后的標(biāo)簽傳播算法對小社團(tuán)的敏感度與解的穩(wěn)定性,不完全更新條件使算法更加省時；船舶柴油機(jī)故障數(shù)據(jù)集上的聚類試驗結(jié)果表明,改進(jìn)后的算法不容易遺漏規(guī)模較小的類,對故障診斷中樣本不均的情況同樣適用。4.針對故障診斷過程中單次聚類方法容易引起信息損失,多重聚類方法需要調(diào)節(jié)預(yù)設(shè)參數(shù)或方法的問題,利用標(biāo)簽傳播算法可能獲得多種解的特點,提出了基于多次標(biāo)簽傳播的船舶柴油機(jī)故障診斷方法。使用改進(jìn)后的標(biāo)簽傳播算法對船舶柴油機(jī)的故障數(shù)據(jù)多次聚類,整合得到的多個結(jié)果或確認(rèn)得到的唯一結(jié)果作為最終聚類結(jié)果,利用得到的聚類中心判斷待識別樣本類型。診斷實例和影響因素試驗結(jié)果表明,該方法無需修改預(yù)設(shè)參數(shù),信息損失較單次聚類少,運行時間短且具有較高的準(zhǔn)確率和較好的穩(wěn)定性,能夠識別出歷史數(shù)據(jù)中不存在的故障類型。
[Abstract]:As a new technology that combines many kinds of knowledge, such as mathematics, physics, computer graphics and sociology, the complex network is an important focus of researchers in all fields of the 21st century. The complex network consists of a large number of nodes and edges, and most of the real networks are composed of a plurality of nodes which are densely connected and sparse with each other, that is, the complex network has a community structure. The community detection is an important research direction of the complex network theory, which helps people to understand the complex system and its representative from the point of view. The Newman fast algorithm and the tag propagation algorithm are two classical community detection methods, which are of general interest due to the rapid detection speed and no need to pre-specify the number of associations. The application of community detection method is one of the important means to solve the problem of the fault diagnosis of marine diesel engine. The marine diesel engine is the heart of the ship, and the advantage of the Newman fast algorithm and the tag propagation algorithm is of great significance to the maintenance and navigation safety of the marine diesel engine. Based on the demand of practical application, this paper studies the extension and improvement strategy of label propagation algorithm and the method of fault diagnosis of marine diesel engine based on community detection theory. The main research work includes the following aspects. This paper presents a new method for fault diagnosis of marine diesel engine based on the Newman fast algorithm. using the sample as the node, the similarity between the samples is the edge weight, the complex network with the right is constructed, and the standard function in the Newman fast algorithm is used as a criterion function of the hierarchical clustering of the bottom up, a clustering method model is established, and the data aggregation of the fault sample of the marine diesel engine is carried out, and the classification result is used to treat the identification sample to identify the fault type. The results of the test show that the method has the advantages of low requirement for initial conditions such as class number, short operation time, high accuracy and certain stability, and can identify the type of fault that does not exist in the historical data. In order to improve the practicability of the label propagation algorithm, the label propagation algorithm is extended so that it is applicable to the network, so that it can be used in the fault data aggregation of the marine diesel engine. By analyzing the three key factors of the label propagation algorithm, the label initial assignment, the label propagation rule and the propagation termination condition are analyzed, the probability of the two adjacent nodes and the community is calculated according to the principle of the multiple edges, the label propagation rule and the termination condition of the label propagation are weighted, thereby promoting the tag propagation algorithm to the right. The results of the network community survey show that the extended label propagation algorithm is applicable to the network community detection; meanwhile, the test results of the classical test data set and the data set of the system fault data of the diesel engine show that the generalized label propagation algorithm is suitable for the data aggregation. In order to solve the problem of trivial solution in the process of label propagation, a label propagation algorithm based on the percolation transition prediction process is proposed. The randomness of the original label propagation algorithm leads to the occurrence of the trivial solution, which affects the speed and the accuracy of the algorithm. The transformation label propagation process is a network construction process, and the percolation transition phenomenon in the random network generation process is linked with the occurrence of the trivial solution, so that the occurrence of the trivial solution is reduced by adding the percolation transition prediction process in the label propagation process. the concept of promoting the purity of the neighbor is to the right network and the calculation time is saved taking into account the incomplete update condition of the node degree of the updated tag. The results of the network test show that the improved algorithm for the sensitivity and solution stability of the small community and the incomplete updating condition make the algorithm more efficient. The results of the poly-type test on the fault data set of the marine diesel engine show that the improved algorithm is not easy to miss the smaller class, The same applies to the case of non-uniform samples in the fault diagnosis. Aiming at the problem that the single-time poly-class method in the fault diagnosis process can easily cause the loss of information, the multi-aggregation method needs to adjust the problem of the preset parameters or the method, and the characteristic of various solutions can be obtained by using the label propagation algorithm, and the fault diagnosis method of the marine diesel engine based on the multi-label propagation is proposed. and using the improved label propagation algorithm to make the fault data of the marine diesel engine multiple times, and the obtained result or the confirmed unique result is used as the final polytype result, and the obtained polytype center judges the type of the sample to be identified. The experimental results of the diagnosis and the influencing factors show that the method does not need to modify the preset parameters, the information loss is less than that of the single sub-group, the running time is short and the operation time is short and has high accuracy and good stability, and the fault type that does not exist in the historical data can be identified.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：U672
[Abstract]:As a new technology that combines many kinds of knowledge, such as mathematics, physics, computer graphics and sociology, the complex network is an important focus of researchers in all fields of the 21st century. The complex network consists of a large number of nodes and edges, and most of the real networks are composed of a plurality of nodes which are densely connected and sparse with each other, that is, the complex network has a community structure. The community detection is an important research direction of the complex network theory, which helps people to understand the complex system and its representative from the point of view. The Newman fast algorithm and the tag propagation algorithm are two classical community detection methods, which are of general interest due to the rapid detection speed and no need to pre-specify the number of associations. The application of community detection method is one of the important means to solve the problem of the fault diagnosis of marine diesel engine. The marine diesel engine is the heart of the ship, and the advantage of the Newman fast algorithm and the tag propagation algorithm is of great significance to the maintenance and navigation safety of the marine diesel engine. Based on the demand of practical application, this paper studies the extension and improvement strategy of label propagation algorithm and the method of fault diagnosis of marine diesel engine based on community detection theory. The main research work includes the following aspects. This paper presents a new method for fault diagnosis of marine diesel engine based on the Newman fast algorithm. using the sample as the node, the similarity between the samples is the edge weight, the complex network with the right is constructed, and the standard function in the Newman fast algorithm is used as a criterion function of the hierarchical clustering of the bottom up, a clustering method model is established, and the data aggregation of the fault sample of the marine diesel engine is carried out, and the classification result is used to treat the identification sample to identify the fault type. The results of the test show that the method has the advantages of low requirement for initial conditions such as class number, short operation time, high accuracy and certain stability, and can identify the type of fault that does not exist in the historical data. In order to improve the practicability of the label propagation algorithm, the label propagation algorithm is extended so that it is applicable to the network, so that it can be used in the fault data aggregation of the marine diesel engine. By analyzing the three key factors of the label propagation algorithm, the label initial assignment, the label propagation rule and the propagation termination condition are analyzed, the probability of the two adjacent nodes and the community is calculated according to the principle of the multiple edges, the label propagation rule and the termination condition of the label propagation are weighted, thereby promoting the tag propagation algorithm to the right. The results of the network community survey show that the extended label propagation algorithm is applicable to the network community detection; meanwhile, the test results of the classical test data set and the data set of the system fault data of the diesel engine show that the generalized label propagation algorithm is suitable for the data aggregation. In order to solve the problem of trivial solution in the process of label propagation, a label propagation algorithm based on the percolation transition prediction process is proposed. The randomness of the original label propagation algorithm leads to the occurrence of the trivial solution, which affects the speed and the accuracy of the algorithm. The transformation label propagation process is a network construction process, and the percolation transition phenomenon in the random network generation process is linked with the occurrence of the trivial solution, so that the occurrence of the trivial solution is reduced by adding the percolation transition prediction process in the label propagation process. the concept of promoting the purity of the neighbor is to the right network and the calculation time is saved taking into account the incomplete update condition of the node degree of the updated tag. The results of the network test show that the improved algorithm for the sensitivity and solution stability of the small community and the incomplete updating condition make the algorithm more efficient. The results of the poly-type test on the fault data set of the marine diesel engine show that the improved algorithm is not easy to miss the smaller class, The same applies to the case of non-uniform samples in the fault diagnosis. Aiming at the problem that the single-time poly-class method in the fault diagnosis process can easily cause the loss of information, the multi-aggregation method needs to adjust the problem of the preset parameters or the method, and the characteristic of various solutions can be obtained by using the label propagation algorithm, and the fault diagnosis method of the marine diesel engine based on the multi-label propagation is proposed. and using the improved label propagation algorithm to make the fault data of the marine diesel engine multiple times, and the obtained result or the confirmed unique result is used as the final polytype result, and the obtained polytype center judges the type of the sample to be identified. The experimental results of the diagnosis and the influencing factors show that the method does not need to modify the preset parameters, the information loss is less than that of the single sub-group, the running time is short and the operation time is short and has high accuracy and good stability, and the fault type that does not exist in the historical data can be identified.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2015
【分類號】：U672
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